Enhance documentation for Docker MCP Toolkit: clarify migration steps, improve structure, and summarize accomplishments across multiple sections.

Author: madeline-underwood

content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/1-overview.md

@@ -38,7 +38,7 @@ These steps are well understood, but they can require careful review across code
 
 The Docker MCP Toolkit is a management interface in Docker Desktop that lets you discover, configure, and run containerized MCP (Model Context Protocol) servers. It connects these servers to AI coding assistants through a unified gateway.
 
-For Arm migration, three MCP servers work together:
+Each MCP server provides specialized capabilities that work together to support the migration workflow. For Arm migration, three MCP servers work together:
 
 - **Arm MCP Server**:
   Provides migration-focused tools, including:
@@ -55,12 +55,12 @@ For Arm migration, three MCP servers work together:
 
 When connected to the Docker MCP Toolkit, an AI coding assistant like GitHub Copilot can coordinate a structured migration workflow:
 
-1. Verify whether container base images support `linux/arm64` using `check_image` or `skopeo`.
-2. Scan the codebase with `migrate_ease_scan` to identify AVX2 intrinsics, x86-specific flags, and other portability considerations.
-3. Use `knowledge_base_search` to find appropriate Arm SIMD equivalents for every x86 intrinsic.
-4. Refactor the code with architecture-specific accuracy.
-5. Update Dockerfiles and build configurations for Arm comptibility. 
-6. Create a pull request with the proposed changes using the GitHub MCP Server.
+- Verify whether container base images support `linux/arm64` using `check_image` or `skopeo`
+- Scan the codebase with `migrate_ease_scan` to identify AVX2 intrinsics, x86-specific flags, and other portability considerations
+- Use `knowledge_base_search` to find appropriate Arm SIMD equivalents for every x86 intrinsic
+- Refactor the code with architecture-specific accuracy
+- Update Dockerfiles and build configurations for Arm compatibility
+- Create a pull request with the proposed changes using the GitHub MCP Server
 
 ## What you will build in this Learning Path
 
@@ -70,10 +70,17 @@ The demo repository is available at [github.com/JoeStech/docker-blog-arm-migrati
 
 You will:
 
-1. Set up Docker MCP Toolkit with the Arm, GitHub, and Sequential Thinking MCP servers.
-2. Connect VS Code with GitHub Copilot to the MCP Gateway.
-3. Analyze the x86-specific portions of the codebase.
-4. Use AI-assisted MCP workflows to update intrinsics and container configuration.
-5. Review and validate the pull request generated by the AI agent.
+- Set up Docker MCP Toolkit with the Arm, GitHub, and Sequential Thinking MCP servers
+- Connect VS Code with GitHub Copilot to the MCP Gateway
+- Analyze the x86-specific portions of the codebase
+- Use AI-assisted MCP workflows to update intrinsics and container configuration
+- Review and validate the pull request generated by the AI agent
 
-In the next section, you will install and configure the Docker MCP Toolkit.
+## What you've accomplished and what's next
+
+You now understand:
+- Why Arm migration requires more than rebuilding containers when architecture-specific code is present
+- How the Docker MCP Toolkit connects AI assistants to specialized migration tools
+- The structured workflow that GitHub Copilot uses to automate migration tasks
+
+Next, you'll install and configure the Docker MCP Toolkit with the three required MCP servers.

content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/2-setup.md

@@ -90,6 +90,12 @@ If the setup is correct, Copilot will list tools from:
 
 This confirms that tool invocation through the MCP Gateway is working.
 
-Your environment is now ready for AI-assisted migration.
+## What you've accomplished and what's next
 
-In the next section, you will examine the demo application and identify the architecture-specific elements that require adaptation for Arm64.
+You have:
+- Enabled the Docker MCP Toolkit in Docker Desktop
+- Configured three MCP servers: Arm MCP Server, GitHub MCP Server, and Sequential Thinking MCP Server
+- Connected VS Code with GitHub Copilot to the MCP Gateway
+- Verified that Copilot can access migration tools
+
+Next, you'll examine the demo application to identify x86-specific code that needs adaptation for Arm64.

content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/3-understand-the-demo.md

@@ -99,4 +99,11 @@ is specific to x86 register structure. On Arm, reduction is implemented using NE
 On newer Arm platforms supporting SVE or SVE2 (for example Neoverse V1/V2 based platforms), wider vector lengths may be available. SVE uses a vector-length-agnostic (VLA) model, which differs from fixed-width AVX2 and NEON programming. The Arm MCP Server knowledge base can help determine the appropriate approach for your target platform.
 {{% /notice %}}
 
-In the next section, you will use GitHub Copilot with the Docker MCP Toolkit to automate the entire migration.
+## What you've accomplished and what's next
+
+You have:
+- Examined a legacy x86 application with AVX2 intrinsics
+- Identified the architecture-specific elements: base image, compiler flags, SIMD headers, and intrinsic functions
+- Understood how vector width differences between AVX2 (256-bit) and NEON (128-bit) affect the migration approach
+
+Next, you'll use GitHub Copilot with the Docker MCP Toolkit to automate the migration process.

content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/4-run-migration.md

@@ -136,14 +136,22 @@ You can see an example PR at [github.com/JoeStech/docker-blog-arm-migration/pull
 After migration, you should see:
 
 **Dockerfile updates**:
-- Replaced `centos:6` with `ubuntu:22.04`.
-- Added `TARGETARCH` for multi-architecture builds.
-- Changed `-mavx2` to `-march=armv8-a+simd` for Arm builds.
+- Replaced `centos:6` with `ubuntu:22.04`
+- Added `TARGETARCH` for multi-architecture builds
+- Changed `-mavx2` to `-march=armv8-a+simd` for Arm builds
 
 **Source code updates**:
-- Added `#ifdef __aarch64__` architecture guards.
-- Replaced all `_mm256_*` AVX2 intrinsics with NEON equivalents (`vld1q_f64`, `vaddq_f64`, `vmulq_f64`).
-- Adjusted loop strides from 4 (AVX2) to 2 (NEON).
-- Rewrote horizontal reduction using NEON pair-wise addition.
+- Added `#ifdef __aarch64__` architecture guards
+- Replaced all `_mm256_*` AVX2 intrinsics with NEON equivalents (`vld1q_f64`, `vaddq_f64`, `vmulq_f64`)
+- Adjusted loop strides from 4 (AVX2) to 2 (NEON)
+- Rewrote horizontal reduction using NEON pair-wise addition
 
-In the next section, you will build, test, and validate the migrated application on Arm.
+## What you've accomplished and what's next
+
+You have:
+- Provided migration instructions to GitHub Copilot
+- Observed the AI-driven workflow using MCP server tools
+- Reviewed the automated changes: container image updates, intrinsic mapping, and compiler flag adjustments
+- Seen how the GitHub MCP Server creates a pull request with all migration changes
+
+Next, you'll build and validate the migrated application on Arm64 hardware.

content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/5-validate-and-next-steps.md

@@ -117,14 +117,14 @@ The Docker MCP Toolkit and Arm MCP Server support more than the example migratio
 - **Knowledge base**: The `knowledge_base_search` tool covers all content from [learn.arm.com](https://learn.arm.com) Learning Paths, intrinsics documentation, and software compatibility information.
 - **Dynamic MCP**: AI agents can discover and add new MCP servers from the Docker MCP Catalog during a conversation without manual configuration.
 
-## Summary
+## What you've accomplished
 
 In this Learning Path, you:
 
-1. Installed and configured the Docker MCP Toolkit with the Arm MCP Server, GitHub MCP Server, and Sequential Thinking MCP Server.
-2. Connected VS Code with GitHub Copilot to the MCP Gateway.
-3. Examined architecture-specific elements in a legacy x86 AVX2 application.
-4. Used AI-assisted MCP tools to analyze, refactor, and update the codebase for Arm64.
-5. Built and validated the migrated application on Arm64.
+- Installed and configured the Docker MCP Toolkit with the Arm MCP Server, GitHub MCP Server, and Sequential Thinking MCP Server
+- Connected VS Code with GitHub Copilot to the MCP Gateway
+- Examined architecture-specific elements in a legacy x86 AVX2 application
+- Used AI-assisted MCP tools to analyze, refactor, and update the codebase for Arm64
+- Built and validated the migrated application on Arm64
 
 The Docker MCP Toolkit enables AI assistants to invoke structured migration tools inside the containerized Arm MCP server. This approach reduces manual lookup and repetitive refactoring work while keeping developers in control of review and validation.

content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md

@@ -1,16 +1,10 @@
 ---
 title: Automate x86 to Arm Migration with Docker MCP Toolkit, VS Code and GitHub Copilot
-
-draft: true
-cascade:
-    draft: true
 
 description: Learn how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot to automate container and code migration from x86 to Arm64. Through a hands-on example, migrate a legacy C++ application with AVX2 intrinsics to Arm NEON.
 
 minutes_to_complete: 45
 
-who_is_this_for: This is an advanced topic for developers and DevOps engineers who want to automate the migration of containerized applications from x86 to Arm64 using AI-powered tools in the Docker MCP Toolkit.
-
 learning_objectives:
   - Describe how the Model Context Protocol (MCP) enables AI coding assistants to invoke structured migration tools through the Arm MCP server
   - Explain how the Docker MCP Toolkit connects AI coding assistants to Arm MCP server
@@ -44,10 +38,6 @@ operatingsystems:
     - macOS
 
 further_reading:
-    - resource:
-        title: "Automate x86 to Arm Migration with Docker MCP Toolkit, VS Code, and GitHub Copilot"
-        link: https://www.docker.com/blog/automate-arm-migration-docker-mcp-copilot/
-        type: blog
     - resource:
         title: Docker MCP Toolkit Documentation
         link: https://docs.docker.com/ai/mcp-catalog-and-toolkit/toolkit/